Applied AI Scientist - Job DescriptionJob Title Applied AI Scientist
Location [City/Remote/Hybrid]
Employment Type Full-time / Contract
Job Summary We are seeking an Applied AI Scientist to research, design, develop, and deploy AI solutions that address real-world business challenges. The ideal candidate combines expertise in machine learning, deep learning, natural language processing (NLP), computer vision, and generative AI with strong problem-solving and software engineering skills. This role involves translating research into production-ready AI applications, collaborating with cross-functional teams, and driving innovation across AI initiatives.
Key Responsibilities - Research, design, and develop AI and machine learning solutions for business and product use cases.
- Build, train, fine-tune, evaluate, and optimize machine learning and deep learning models.
- Develop applications using large language models (LLMs), multimodal AI, and generative AI technologies.
- Design and implement Retrieval-Augmented Generation (RAG), AI agents, and intelligent automation solutions.
- Conduct experiments to evaluate model accuracy, robustness, scalability, and business impact.
- Analyze structured and unstructured data to derive insights and improve model performance.
- Collaborate with data scientists, AI engineers, software developers, product managers, and business stakeholders.
- Translate research findings into scalable, production-ready AI systems.
- Implement model monitoring, evaluation, and continuous improvement processes.
- Publish technical documentation, research findings, and reusable AI assets where appropriate.
- Stay current with advancements in AI, foundation models, reinforcement learning, and emerging technologies.
Required Qualifications - Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Statistics, or a related field.
- 3-8+ years of experience in AI research, applied machine learning, or data science.
- Strong knowledge of machine learning, deep learning, NLP, computer vision, and statistical modeling.
- Experience developing and deploying production AI applications.
- Proficiency in Python and experience with AI/ML frameworks such as PyTorch, TensorFlow, and Scikit-learn.
- Hands-on experience with LLMs, prompt engineering, embeddings, vector databases, and RAG architectures.
- Strong understanding of experimental design, model evaluation, and performance optimization.
- Excellent analytical, communication, and problem-solving skills.
Preferred Qualifications - Experience with multimodal AI, reinforcement learning, or agentic AI systems.
- Familiarity with distributed training and large-scale model deployment.
- Experience with cloud AI platforms and MLOps practices.
- Publications, patents, or contributions to open-source AI projects.
- Experience in industries such as healthcare, finance, manufacturing, retail, or telecommunications.
- Professional certifications in AI, machine learning, or cloud technologies.
Technical Skills - Python
- SQL
- Machine Learning
- Deep Learning
- Natural Language Processing (NLP)
- Computer Vision
- Large Language Models (LLMs)
- Generative AI
- Prompt Engineering
- Retrieval-Augmented Generation (RAG)
- AI Agents
- Reinforcement Learning (preferred)
- PyTorch
- TensorFlow
- Scikit-learn
- Hugging Face Transformers
- LangChain
- LlamaIndex
- Vector Databases (Pinecone, Weaviate, Chroma, FAISS, Milvus)
- OpenAI API
- Google Gemini API
- Anthropic API
- FastAPI
- Docker
- Kubernetes
- Git
- MLflow
- REST APIs
- AWS, Microsoft Azure, or Google Cloud
Soft Skills - Research and analytical thinking
- Problem-solving
- Innovation and creativity
- Communication and presentation
- Cross-functional collaboration
- Critical thinking
- Project management
- Adaptability
- Continuous learning
Key Deliverables - AI models and production-ready AI applications
- Research prototypes and proof of concepts (POCs)
- Model evaluation and benchmarking reports
- AI solution architectures
- Technical documentation
- Experimentation reports
- Reusable AI components and frameworks
- Business impact assessments
Success Metrics - Model accuracy, precision, recall, and other performance metrics
- Successful deployment of AI solutions into production
- Business impact and measurable value delivered
- Scalability, reliability, and efficiency of AI systems
- Innovation through research contributions and new AI capabilities
- Reduction in model inference latency and operational costs
- Stakeholder satisfaction and adoption of AI solutions
- On-time delivery of AI research and development milestones
